The present disclosure relates generally to graphical models, and more specifically, to influence filtering in graphical models.
Graphical models are widely used in artificial intelligence and machine learning. They are often used to represent the patterns of dependence between variables and to predict the distribution of some variables given some other variables. Much of the literature on graphical models has focused largely on learning graphical models from statistical resources. These might include correlations or conditional dependencies between variables. Another type of graphical model is one that is constructed based on one or more knowledge sources. For example, a phrase in a medical textbook such as “pneumonia causes fever in most patients” may be extracted and used to generate the knowledge that fever is present more often in patients that also have pneumonia, as compared to patients that do not have pneumonia. Post processing, such as relation extraction from natural language processing and/or querying a database of relations, may be performed on the extracted phrase to generate one or more relations associated with the phrase. The output of the post processing is a relational resource includes a set of relations that are then used to build a graphical model. When constructing graphical models from a relational resource, some issues arise that do not arise when constructing graphical models from a statistical resource. In particular, sets of relations from the knowledge source may be redundant with each other, causing an overestimation of the influence of one variable on another. Redundant sets of relations do not occur when graphical models are generated directly from statistical data, as influence can be estimated directly.